ABSTRACT. Alzheimer?s Disease (AD) is a fatal neurodegenerative disease affecting tens of millions of people. A top research priority in AD is to find a biomarker sensitive to early disease and disease progression because that will likely provide the best opportunities for searching and evaluating effective treatments for this currently incurable brain disease. Human brain relies on cerebral blood flow (CBF) for its energy supply and waste removal. Previous research has indicated that CBF reductions cause neuron inactivity and neurotoxic waste accumulation and subsequently neuron death, which may eventually lead to AD dementia. Measuring CBF and following its longitudinal course may then provide a highly potential biomarker for early AD and its progression. Arterial spin labeling (ASL) perfusion MRI is a technique for quantifying CBF without using exogenous tracers. Because it is relatively cheaper and can be repeated many times, it is well suited for longitudinal AD research. Sensitivity of ASL MRI to AD and prodromal AD has been shown in many cross-sectional studies (comparing AD to controls) by other groups and us. But several important questions still remain unanswered including the prediction power of ASL CBF for early AD and AD progression, the longitudinal CBF change patterns, and sex difference of CBF in AD. Addressing those questions needs large size longitudinal data and expertise for analyzing ASL MRI data due to the low signal-to-noise-ratio (SNR). The purpose of this project is to address those open questions by leveraging our extensive expertise on ASL MRI and the accumulating longitudinal ASL data from ADNI (a large ongoing multi-site AD neuroimaging study). The novel information or outcome from this project will include the prediction power of ASL CBF for early AD, CBF change rate when disease progresses or reverts, gender effects in AD and its progression, and a next-generation ASL MRI processing algorithm based on deep-machine learning (DL), and a DL-based AD prediction model. We will first confirm our previous ADNI ASL CBF findings using larger sample and updated methods (not available before). We will then check sensitivity of ASL CBF for tracking and predicting disease progression or cognitive declines. Gender effects on CBF in AD will be explicitly examined, which may reveal a clue for the higher prevalence of AD in females. We will revisit those studies using the DL-based ASL denoising algorithm. Pursuing those aims will help establishing ASL CBF as an AD biomarker and provide a versatile AD prediction model using ASL CBF as well as other valuable biomarkers provided in ADNI. Developing and sharing the DL ASL MRI denoising method will benefit not only AD research but also the various scientific projects based on ASL MRI. The feasibility of this innovative but clinically important project is ensured by our decades of experience and the substantial pilot investigations in each aspect of the study, including ASL MRI, AD ASL study, and machine learning.